Senior Analytics Engineer (AI & Business Intelligence)

Yuxi Global is seeking a Senior AI Analytics Engineer / Agentic BI Lead to support a fast-moving business intelligence team working on large-scale product and operational analytics.

This role is designed for a high-performing analytics professional who combines advanced SQL expertise enterprise-scale data experience product-minded analytics judgment and hands-on experience applying AI to business intelligence workflows. The successful candidate will not operate as a traditional Business Analyst or dashboard-only reporting resource. This person must be able to own ambiguous analytics problems from stakeholder discovery through SQL development validation AI-assisted workflow design automation delivery and business adoption.

The ideal candidate has experience working with large data ecosystems preferably datasets with hundreds of millions of rows or more and can reason deeply about data quality performance metric definitions root-cause analysis and stakeholder decision-making. This role requires someone who can work independently ramp quickly communicate clearly and drive outcomes with minimal hand-holding.

This is a builder-owner role. The right person can sit with business and technical stakeholders understand the real problem find the right data write and optimize the SQL validate the result determine where AI or automation can help build or prototype the workflow test the output and drive simple terms: this person turns BI ambiguity into trusted scalable AI-enabled analytics capability.

Core Responsibilities

Own end-to-end analytics initiatives from stakeholder discovery through implementation validation automation and business adoption.

Partner with business BI data and technical stakeholders to clarify objectives define success metrics and convert ambiguous requests into actionable analytics work.

Design write optimize and validate advanced SQL queries against large-scale enterprise datasets.

Develop scalable analytics solutions that support operational decision-making product insights root-cause analysis and recurring business reporting.

Apply AI and automation to improve BI workflows including natural language-to-SQL BI copilots automated insight generation anomaly/root-cause workflows and data-quality monitoring.

Build evaluate test and refine AI-assisted analytics workflows to ensure accuracy explainability repeatability and business trust.

Identify opportunities to reduce manual reporting effort streamline stakeholder requests improve analytical throughput and increase BI team productivity.

Validate analytical outputs through rigorous data-quality checks metric reconciliation source-of-truth review and stakeholder confirmation.

Collaborate with BI engineers data engineers product analysts and business stakeholders to align data models metric definitions and reporting logic.

Create clear documentation for analytics logic SQL assumptions data lineage AI workflow behavior known limitations and stakeholder-facing outputs.

Operate independently in a fast-paced environment with limited onboarding support while proactively identifying risks dependencies blockers and decisions needed.

Communicate insights tradeoffs risks and recommendations clearly to both technical and non-technical stakeholders.

Support adoption of analytics and AI-enabled BI solutions by training stakeholders gathering feedback measuring usage and iterating on workflows.

Help define reusable patterns for agentic BI delivery including evaluation methods validation checklists governance practices and automation playbooks.

Mentor analysts or engineers on advanced SQL AI-assisted analytics workflows business framing and end-to-end project ownership.

Qualifications :

8 years of professional experience in business intelligence analytics engineering data analytics product analytics data engineering or related fields.

Advanced SQL expertise including experience working with large complex enterprise-scale datasets.

Experience analyzing datasets at significant scale ideally involving hundreds of millions of rows or similarly complex data environments.

Demonstrated ability to own analytics projects end-to-end from stakeholder requirements and problem framing through implementation validation delivery and adoption.

Hands-on experience applying AI automation or agentic workflows to analytics reporting data quality or decision-support use cases.

Experience with one or more AI-enabled BI patterns such as natural language-to-SQL BI copilots automated insight generation root-cause analysis anomaly detection data-quality monitoring or analytics workflow automation.

Strong understanding of data validation metric reconciliation source-of-truth alignment and data-quality practices.

Ability to work independently ramp quickly and drive outcomes without heavy day-to-day management oversight.

Strong stakeholder management skills including the ability to clarify ambiguous requests challenge assumptions and guide business users toward better analytical questions.

Strong written and verbal communication skills in English.

Bachelors degree in Computer Science Data Analytics Statistics Information Systems Engineering Business Analytics Mathematics Economics or equivalent practical experience.

Preferred Qualifications

Experience supporting BI product analytics marketplace analytics digital platform analytics mobile/app ecosystem analytics subscription analytics developer ecosystem analytics or large-scale consumer product analytics.

Experience with Google Cloud analytics tools such as BigQuery GoogleSQL Looker LookML Dataform Vertex AI or related cloud data and AI services.

Experience with BI and analytics tools such as Looker Tableau Power BI Mode Hex Sigma Superset or similar platforms.

Experience with Python for analytics automation data validation workflow orchestration or AI-assisted analysis.

Experience designing or evaluating text-to-SQL systems retrieval-augmented analytics workflows LLM-based analytics agents or AI-assisted BI copilots.

Experience building evaluation harnesses or test approaches for AI-generated analytics outputs.

Experience with analytics engineering tools such as dbt Airflow Dagster Prefect Dataform or similar workflow and transformation platforms.

Experience with data quality frameworks anomaly detection metric monitoring or automated validation workflows.

Experience working with globally distributed teams supporting U.S. business stakeholders preferably aligned to Pacific Time working hours.

Experience in consulting staff augmentation managed services or embedded client-facing delivery environments.

Experience as a contractor or employee at Google and especially Google Play in a business intelligence role is a plus.

Technical Skills

Candidates should demonstrate familiarity with one or more tools methodologies or frameworks in the following areas:

Core Skills: Advanced SQL BigQuery Looker LookML Data Modeling Product Analytics Analytics Engineering AI Agents LLM Workflows Python GCP Vertex AI dbt Airflow Data Quality Stakeholder Management.

Business Intelligence & Analytics

Advanced SQL GoogleSQL BigQuery Looker LookML Semantic Layers Data Modeling Metric Definition Dashboard Development Self-Service Analytics Stakeholder Reporting.

Data Visualization & BI Tools

Tableau Power BI Mode Hex Sigma Superset.

AI & Agentic Analytics

Natural Language-to-SQL BI Copilots LLM-Assisted Analytics Workflows Agentic Analytics Workflows Prompt Design for Analytics Automated Insight Generation AI-Driven Root Cause Analysis Anomaly Detection AI-Assisted Data Quality Monitoring.

AI Agent Development & Governance

Tool-Calling & Function-Calling Patterns Agent Evaluation Regression Testing for AI Outputs Human-in-the-Loop Review.

Programming & Automation

Python SQL APIs Workflow Automation Scripting Data Validation Scripts Scheduled Jobs Reusable Analytics Utilities Notebook Environments Automation of Recurring Reports.

Cloud & Data Platforms

Google Cloud Platform (GCP) BigQuery Vertex AI Cloud Functions Cloud Run Cloud Storage Snowflake Databricks Redshift AWS Data Services Azure Data Services PostgreSQL MySQL NoSQL Data Stores.

Data Workflow & Transformation

dbt Dataform Airflow Dagster Prefect Scheduled Pipelines Data Transformation Workflows Version-Controlled Analytics Code GitHub GitLab CI/CD for Analytics Testing & Deployment Practices.

Quality Governance & Trust

Metric Governance Data Validation Data Quality Checks Documentation Standards Access Controls Permission-Aware Analytics Auditability Analytical Reproducibility AI Output Validation Hallucination Detection False-Positive & False-Negative Review Business Sign-Off Workflows.

Product & Business Analytics

Product Metrics Marketplace Analytics Funnel Analysis Cohort Analysis Retention Analysis Revenue Analysis Engagement Metrics Operational Analytics Experimentation Analysis A/B Testing Concepts Root Cause Analysis Executive Insight Narratives.

Soft Skills

Exceptional ownership mindset with the ability to drive an ambiguous analytics problem from idea to adoption.

Highly proactive and self-directed; able to make progress without waiting for perfect instructions.

Strong business judgment with the ability to distinguish between a reporting request and the underlying business question.

Excellent stakeholder communication and facilitation skills.

Ability to challenge assumptions constructively and guide stakeholders toward better metrics better questions and better decisions.

Strong attention to detail especially around data accuracy metric definitions and validation.

Comfortable working in ambiguity incomplete context and fast-changing stakeholder environments.

Ability to balance speed with analytical rigor and explain tradeoffs clearly.

Strong technical curiosity and willingness to experiment with AI-enabled workflows while maintaining discipline around trust and correctness.

Low-ego collaboration style with the ability to work effectively across BI data engineering product operations and business teams.

Organizational Competencies

Remote Collaboration

Thrives in distributed environments and effectively manages work across geographically diverse teams stakeholders and time zones.

Continuous Learning

Demonstrates curiosity and a commitment to staying current with AI analytics engineering data platforms business intelligence and automation practices.

Cultural Alignment

Embodies Veritas Automatas values of innovation integrity accountability collaboration and excellence.

Strategic Thinking

Understands how analytics AI automation and trusted data products contribute to business growth operational efficiency and better decision-making.

Adaptability

Excels in dynamic environments with evolving priorities ambiguous requirements and changing stakeholder needs.

Representation & Advocacy

Represents the organization professionally with clients partners stakeholders and technical teams.

Remote Work and Time Zone Expectations

This position is primarily remote and is designed to support collaboration across distributed teams clients and stakeholders in multiple geographic locations.

The successful candidate must be comfortable working independently in a remote environment while maintaining strong communication responsiveness and accountability.

The role requires alignment with U.S. Pacific Time working hours. Candidates located in Canada or LATAM are preferred provided they can support the necessary working-hour overlap.

Regular participation in virtual meetings stakeholder reviews planning sessions working sessions and delivery checkpoints is expected.

Additional Information :

We are an equal-opportunity employer and prohibit discrimination and harassment of any kind. All employment decisions are based on business needs job requirements and individual qualifications without regard to race color religion gender sexual orientation age disability or any other status protected by the laws or regulations in our locations.

Remote Work :

Yes

Employment Type :

Full-time